Overview

Dataset statistics

Number of variables15
Number of observations1197
Missing cells506
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory140.4 KiB
Average record size in memory120.1 B

Variable types

DateTime1
Categorical4
Numeric10

Alerts

no_of_style_change is highly imbalanced (60.1%)Imbalance
wip has 506 (42.3%) missing valuesMissing
idle_time is highly skewed (γ1 = 20.54542523)Skewed
over_time has 31 (2.6%) zerosZeros
incentive has 604 (50.5%) zerosZeros
idle_time has 1179 (98.5%) zerosZeros
idle_men has 1179 (98.5%) zerosZeros

Reproduction

Analysis started2024-03-06 20:34:36.086034
Analysis finished2024-03-06 20:34:43.228718
Duration7.14 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

date
Date

Distinct59
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Minimum2015-01-01 00:00:00
Maximum2015-03-11 00:00:00
2024-03-06T15:34:43.287329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:43.379235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

quarter
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Quarter1
360 
Quarter2
335 
Quarter4
248 
Quarter3
210 
Quarter5
44 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters9576
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQuarter1
2nd rowQuarter1
3rd rowQuarter1
4th rowQuarter1
5th rowQuarter1

Common Values

ValueCountFrequency (%)
Quarter1 360
30.1%
Quarter2 335
28.0%
Quarter4 248
20.7%
Quarter3 210
17.5%
Quarter5 44
 
3.7%

Length

2024-03-06T15:34:43.457598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-06T15:34:43.526242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
quarter1 360
30.1%
quarter2 335
28.0%
quarter4 248
20.7%
quarter3 210
17.5%
quarter5 44
 
3.7%

Most occurring characters

ValueCountFrequency (%)
r 2394
25.0%
Q 1197
12.5%
u 1197
12.5%
a 1197
12.5%
t 1197
12.5%
e 1197
12.5%
1 360
 
3.8%
2 335
 
3.5%
4 248
 
2.6%
3 210
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7182
75.0%
Uppercase Letter 1197
 
12.5%
Decimal Number 1197
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 2394
33.3%
u 1197
16.7%
a 1197
16.7%
t 1197
16.7%
e 1197
16.7%
Decimal Number
ValueCountFrequency (%)
1 360
30.1%
2 335
28.0%
4 248
20.7%
3 210
17.5%
5 44
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
Q 1197
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8379
87.5%
Common 1197
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 2394
28.6%
Q 1197
14.3%
u 1197
14.3%
a 1197
14.3%
t 1197
14.3%
e 1197
14.3%
Common
ValueCountFrequency (%)
1 360
30.1%
2 335
28.0%
4 248
20.7%
3 210
17.5%
5 44
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 2394
25.0%
Q 1197
12.5%
u 1197
12.5%
a 1197
12.5%
t 1197
12.5%
e 1197
12.5%
1 360
 
3.8%
2 335
 
3.5%
4 248
 
2.6%
3 210
 
2.2%

department
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
sweing
691 
finishing
257 
finishing
249 

Length

Max length10
Median length6
Mean length7.4828739
Min length6

Characters and Unicode

Total characters8957
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsweing
2nd rowfinishing
3rd rowsweing
4th rowsweing
5th rowsweing

Common Values

ValueCountFrequency (%)
sweing 691
57.7%
finishing 257
 
21.5%
finishing 249
 
20.8%

Length

2024-03-06T15:34:43.608357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-06T15:34:43.673417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
sweing 691
57.7%
finishing 506
42.3%

Most occurring characters

ValueCountFrequency (%)
i 2209
24.7%
n 1703
19.0%
s 1197
13.4%
g 1197
13.4%
w 691
 
7.7%
e 691
 
7.7%
f 506
 
5.6%
h 506
 
5.6%
257
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8700
97.1%
Space Separator 257
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2209
25.4%
n 1703
19.6%
s 1197
13.8%
g 1197
13.8%
w 691
 
7.9%
e 691
 
7.9%
f 506
 
5.8%
h 506
 
5.8%
Space Separator
ValueCountFrequency (%)
257
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8700
97.1%
Common 257
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2209
25.4%
n 1703
19.6%
s 1197
13.8%
g 1197
13.8%
w 691
 
7.9%
e 691
 
7.9%
f 506
 
5.8%
h 506
 
5.8%
Common
ValueCountFrequency (%)
257
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8957
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 2209
24.7%
n 1703
19.0%
s 1197
13.4%
g 1197
13.4%
w 691
 
7.7%
e 691
 
7.7%
f 506
 
5.6%
h 506
 
5.6%
257
 
2.9%

day
Categorical

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Wednesday
208 
Sunday
203 
Tuesday
201 
Thursday
199 
Monday
199 

Length

Max length9
Median length8
Mean length7.3341688
Min length6

Characters and Unicode

Total characters8779
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowThursday
3rd rowThursday
4th rowThursday
5th rowThursday

Common Values

ValueCountFrequency (%)
Wednesday 208
17.4%
Sunday 203
17.0%
Tuesday 201
16.8%
Thursday 199
16.6%
Monday 199
16.6%
Saturday 187
15.6%

Length

2024-03-06T15:34:43.741078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-06T15:34:43.807234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
wednesday 208
17.4%
sunday 203
17.0%
tuesday 201
16.8%
thursday 199
16.6%
monday 199
16.6%
saturday 187
15.6%

Most occurring characters

ValueCountFrequency (%)
d 1405
16.0%
a 1384
15.8%
y 1197
13.6%
u 790
9.0%
e 617
7.0%
n 610
6.9%
s 608
6.9%
T 400
 
4.6%
S 390
 
4.4%
r 386
 
4.4%
Other values (5) 992
11.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7582
86.4%
Uppercase Letter 1197
 
13.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 1405
18.5%
a 1384
18.3%
y 1197
15.8%
u 790
10.4%
e 617
8.1%
n 610
8.0%
s 608
8.0%
r 386
 
5.1%
h 199
 
2.6%
o 199
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
T 400
33.4%
S 390
32.6%
W 208
17.4%
M 199
16.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 8779
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1405
16.0%
a 1384
15.8%
y 1197
13.6%
u 790
9.0%
e 617
7.0%
n 610
6.9%
s 608
6.9%
T 400
 
4.6%
S 390
 
4.4%
r 386
 
4.4%
Other values (5) 992
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8779
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1405
16.0%
a 1384
15.8%
y 1197
13.6%
u 790
9.0%
e 617
7.0%
n 610
6.9%
s 608
6.9%
T 400
 
4.6%
S 390
 
4.4%
r 386
 
4.4%
Other values (5) 992
11.3%

team
Real number (ℝ)

Distinct12
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4269006
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2024-03-06T15:34:43.880107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4639633
Coefficient of variation (CV)0.53897882
Kurtosis-1.2239057
Mean6.4269006
Median Absolute Deviation (MAD)3
Skewness0.0098475028
Sum7693
Variance11.999042
MonotonicityNot monotonic
2024-03-06T15:34:43.941709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 109
9.1%
2 109
9.1%
1 105
8.8%
4 105
8.8%
9 104
8.7%
10 100
8.4%
12 99
8.3%
7 96
8.0%
3 95
7.9%
6 94
7.9%
Other values (2) 181
15.1%
ValueCountFrequency (%)
1 105
8.8%
2 109
9.1%
3 95
7.9%
4 105
8.8%
5 93
7.8%
6 94
7.9%
7 96
8.0%
8 109
9.1%
9 104
8.7%
10 100
8.4%
ValueCountFrequency (%)
12 99
8.3%
11 88
7.4%
10 100
8.4%
9 104
8.7%
8 109
9.1%
7 96
8.0%
6 94
7.9%
5 93
7.8%
4 105
8.8%
3 95
7.9%

targeted_productivity
Real number (ℝ)

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72963241
Minimum0.07
Maximum0.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2024-03-06T15:34:44.003433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.5
Q10.7
median0.75
Q30.8
95-th percentile0.8
Maximum0.8
Range0.73
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.097890963
Coefficient of variation (CV)0.13416477
Kurtosis5.6137006
Mean0.72963241
Median Absolute Deviation (MAD)0.05
Skewness-2.14415
Sum873.37
Variance0.0095826407
MonotonicityNot monotonic
2024-03-06T15:34:44.068238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.8 540
45.1%
0.7 242
20.2%
0.75 216
 
18.0%
0.65 63
 
5.3%
0.6 57
 
4.8%
0.5 49
 
4.1%
0.35 27
 
2.3%
0.4 2
 
0.2%
0.07 1
 
0.1%
ValueCountFrequency (%)
0.07 1
 
0.1%
0.35 27
 
2.3%
0.4 2
 
0.2%
0.5 49
 
4.1%
0.6 57
 
4.8%
0.65 63
 
5.3%
0.7 242
20.2%
0.75 216
 
18.0%
0.8 540
45.1%
ValueCountFrequency (%)
0.8 540
45.1%
0.75 216
 
18.0%
0.7 242
20.2%
0.65 63
 
5.3%
0.6 57
 
4.8%
0.5 49
 
4.1%
0.4 2
 
0.2%
0.35 27
 
2.3%
0.07 1
 
0.1%

smv
Real number (ℝ)

Distinct70
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.062172
Minimum2.9
Maximum54.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2024-03-06T15:34:44.147296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile2.9
Q13.94
median15.26
Q324.26
95-th percentile30.1
Maximum54.56
Range51.66
Interquartile range (IQR)20.32

Descriptive statistics

Standard deviation10.943219
Coefficient of variation (CV)0.72653659
Kurtosis-0.79534591
Mean15.062172
Median Absolute Deviation (MAD)11.11
Skewness0.40593674
Sum18029.42
Variance119.75405
MonotonicityNot monotonic
2024-03-06T15:34:44.230781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.94 192
16.0%
2.9 108
 
9.0%
22.52 103
 
8.6%
30.1 79
 
6.6%
4.15 76
 
6.3%
18.79 50
 
4.2%
4.6 46
 
3.8%
15.26 44
 
3.7%
25.9 32
 
2.7%
11.61 31
 
2.6%
Other values (60) 436
36.4%
ValueCountFrequency (%)
2.9 108
9.0%
3.9 20
 
1.7%
3.94 192
16.0%
4.08 21
 
1.8%
4.15 76
 
6.3%
4.3 17
 
1.4%
4.6 46
 
3.8%
5.13 26
 
2.2%
10.05 6
 
0.5%
11.41 30
 
2.5%
ValueCountFrequency (%)
54.56 1
0.1%
51.02 1
0.1%
50.89 1
0.1%
50.48 2
0.2%
49.1 1
0.1%
48.84 2
0.2%
48.68 1
0.1%
48.18 1
0.1%
45.67 1
0.1%
42.97 2
0.2%

wip
Real number (ℝ)

MISSING 

Distinct548
Distinct (%)79.3%
Missing506
Missing (%)42.3%
Infinite0
Infinite (%)0.0%
Mean1190.466
Minimum7
Maximum23122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2024-03-06T15:34:44.311989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile358.5
Q1774.5
median1039
Q31252.5
95-th percentile1602
Maximum23122
Range23115
Interquartile range (IQR)478

Descriptive statistics

Standard deviation1837.455
Coefficient of variation (CV)1.5434754
Kurtosis101.70204
Mean1190.466
Median Absolute Deviation (MAD)232
Skewness9.7417863
Sum822612
Variance3376240.9
MonotonicityNot monotonic
2024-03-06T15:34:44.395259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1039 5
 
0.4%
1282 4
 
0.3%
1422 3
 
0.3%
1216 3
 
0.3%
1413 3
 
0.3%
1448 3
 
0.3%
759 3
 
0.3%
1095 3
 
0.3%
1108 3
 
0.3%
1079 3
 
0.3%
Other values (538) 658
55.0%
(Missing) 506
42.3%
ValueCountFrequency (%)
7 1
0.1%
10 1
0.1%
11 1
0.1%
12 1
0.1%
13 1
0.1%
14 1
0.1%
15 1
0.1%
29 1
0.1%
30 1
0.1%
52 1
0.1%
ValueCountFrequency (%)
23122 1
0.1%
21540 1
0.1%
21385 1
0.1%
21266 1
0.1%
16882 1
0.1%
12261 1
0.1%
9792 1
0.1%
8992 1
0.1%
2984 1
0.1%
2698 1
0.1%

over_time
Real number (ℝ)

ZEROS 

Distinct143
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4567.4603
Minimum0
Maximum25920
Zeros31
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2024-03-06T15:34:44.477024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile960
Q11440
median3960
Q36960
95-th percentile10368
Maximum25920
Range25920
Interquartile range (IQR)5520

Descriptive statistics

Standard deviation3348.8236
Coefficient of variation (CV)0.73319161
Kurtosis0.4243643
Mean4567.4603
Median Absolute Deviation (MAD)2760
Skewness0.6732873
Sum5467250
Variance11214619
MonotonicityNot monotonic
2024-03-06T15:34:44.561547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
960 129
 
10.8%
1440 111
 
9.3%
6960 61
 
5.1%
6840 48
 
4.0%
1200 39
 
3.3%
1800 38
 
3.2%
10170 36
 
3.0%
0 31
 
2.6%
3360 30
 
2.5%
4080 30
 
2.5%
Other values (133) 644
53.8%
ValueCountFrequency (%)
0 31
 
2.6%
120 1
 
0.1%
240 6
 
0.5%
360 2
 
0.2%
480 1
 
0.1%
600 4
 
0.3%
720 4
 
0.3%
840 2
 
0.2%
900 2
 
0.2%
960 129
10.8%
ValueCountFrequency (%)
25920 1
 
0.1%
15120 1
 
0.1%
15000 2
 
0.2%
14640 1
 
0.1%
13800 1
 
0.1%
12600 1
 
0.1%
12180 1
 
0.1%
12000 1
 
0.1%
10770 1
 
0.1%
10620 22
1.8%

incentive
Real number (ℝ)

ZEROS 

Distinct48
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.210526
Minimum0
Maximum3600
Zeros604
Zeros (%)50.5%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2024-03-06T15:34:44.641052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q350
95-th percentile88
Maximum3600
Range3600
Interquartile range (IQR)50

Descriptive statistics

Standard deviation160.18264
Coefficient of variation (CV)4.1921077
Kurtosis299.03246
Mean38.210526
Median Absolute Deviation (MAD)0
Skewness15.790746
Sum45738
Variance25658.479
MonotonicityNot monotonic
2024-03-06T15:34:44.725330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 604
50.5%
50 113
 
9.4%
63 61
 
5.1%
45 54
 
4.5%
30 52
 
4.3%
23 38
 
3.2%
38 29
 
2.4%
60 28
 
2.3%
40 27
 
2.3%
75 24
 
2.0%
Other values (38) 167
 
14.0%
ValueCountFrequency (%)
0 604
50.5%
21 1
 
0.1%
23 38
 
3.2%
24 2
 
0.2%
25 1
 
0.1%
26 9
 
0.8%
27 2
 
0.2%
29 1
 
0.1%
30 52
 
4.3%
32 1
 
0.1%
ValueCountFrequency (%)
3600 1
 
0.1%
2880 1
 
0.1%
1440 1
 
0.1%
1200 1
 
0.1%
1080 1
 
0.1%
960 5
 
0.4%
138 1
 
0.1%
119 2
 
0.2%
113 21
1.8%
100 7
 
0.6%

idle_time
Real number (ℝ)

SKEWED  ZEROS 

Distinct12
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73015873
Minimum0
Maximum300
Zeros1179
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2024-03-06T15:34:44.793291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum300
Range300
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.709757
Coefficient of variation (CV)17.40684
Kurtosis442.63816
Mean0.73015873
Median Absolute Deviation (MAD)0
Skewness20.545425
Sum874
Variance161.53791
MonotonicityNot monotonic
2024-03-06T15:34:44.856096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1179
98.5%
3.5 3
 
0.3%
2 2
 
0.2%
5 2
 
0.2%
8 2
 
0.2%
4.5 2
 
0.2%
4 2
 
0.2%
90 1
 
0.1%
150 1
 
0.1%
270 1
 
0.1%
Other values (2) 2
 
0.2%
ValueCountFrequency (%)
0 1179
98.5%
2 2
 
0.2%
3.5 3
 
0.3%
4 2
 
0.2%
4.5 2
 
0.2%
5 2
 
0.2%
6.5 1
 
0.1%
8 2
 
0.2%
90 1
 
0.1%
150 1
 
0.1%
ValueCountFrequency (%)
300 1
 
0.1%
270 1
 
0.1%
150 1
 
0.1%
90 1
 
0.1%
8 2
0.2%
6.5 1
 
0.1%
5 2
0.2%
4.5 2
0.2%
4 2
0.2%
3.5 3
0.3%

idle_men
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36925647
Minimum0
Maximum45
Zeros1179
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2024-03-06T15:34:44.918957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum45
Range45
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.2689873
Coefficient of variation (CV)8.852891
Kurtosis102.96287
Mean0.36925647
Median Absolute Deviation (MAD)0
Skewness9.8550791
Sum442
Variance10.686278
MonotonicityNot monotonic
2024-03-06T15:34:44.978993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 1179
98.5%
10 3
 
0.3%
15 3
 
0.3%
30 3
 
0.3%
20 3
 
0.3%
35 2
 
0.2%
45 1
 
0.1%
37 1
 
0.1%
25 1
 
0.1%
40 1
 
0.1%
ValueCountFrequency (%)
0 1179
98.5%
10 3
 
0.3%
15 3
 
0.3%
20 3
 
0.3%
25 1
 
0.1%
30 3
 
0.3%
35 2
 
0.2%
37 1
 
0.1%
40 1
 
0.1%
45 1
 
0.1%
ValueCountFrequency (%)
45 1
 
0.1%
40 1
 
0.1%
37 1
 
0.1%
35 2
 
0.2%
30 3
 
0.3%
25 1
 
0.1%
20 3
 
0.3%
15 3
 
0.3%
10 3
 
0.3%
0 1179
98.5%

no_of_style_change
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0
1050 
1
114 
2
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1197
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1050
87.7%
1 114
 
9.5%
2 33
 
2.8%

Length

2024-03-06T15:34:45.044221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-06T15:34:45.100749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1050
87.7%
1 114
 
9.5%
2 33
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 1050
87.7%
1 114
 
9.5%
2 33
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1197
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1050
87.7%
1 114
 
9.5%
2 33
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1197
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1050
87.7%
1 114
 
9.5%
2 33
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1197
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1050
87.7%
1 114
 
9.5%
2 33
 
2.8%

no_of_workers
Real number (ℝ)

Distinct61
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.609858
Minimum2
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2024-03-06T15:34:45.169314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q19
median34
Q357
95-th percentile59
Maximum89
Range87
Interquartile range (IQR)48

Descriptive statistics

Standard deviation22.197687
Coefficient of variation (CV)0.6413689
Kurtosis-1.7881079
Mean34.609858
Median Absolute Deviation (MAD)24
Skewness-0.11173973
Sum41428
Variance492.73729
MonotonicityNot monotonic
2024-03-06T15:34:45.249671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 262
21.9%
58 114
 
9.5%
57 109
 
9.1%
59 75
 
6.3%
10 60
 
5.0%
56.5 54
 
4.5%
56 49
 
4.1%
34 43
 
3.6%
9 42
 
3.5%
12 37
 
3.1%
Other values (51) 352
29.4%
ValueCountFrequency (%)
2 6
 
0.5%
4 1
 
0.1%
5 3
 
0.3%
6 1
 
0.1%
7 3
 
0.3%
8 262
21.9%
9 42
 
3.5%
10 60
 
5.0%
11 1
 
0.1%
12 37
 
3.1%
ValueCountFrequency (%)
89 1
 
0.1%
60 7
 
0.6%
59.5 5
 
0.4%
59 75
6.3%
58.5 21
 
1.8%
58 114
9.5%
57.5 25
 
2.1%
57 109
9.1%
56.5 54
4.5%
56 49
4.1%

actual_productivity
Real number (ℝ)

Distinct879
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7350911
Minimum0.23370548
Maximum1.1204375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2024-03-06T15:34:45.453600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.23370548
5-th percentile0.35551319
Q10.65030714
median0.77333333
Q30.85025253
95-th percentile0.97703788
Maximum1.1204375
Range0.88673202
Interquartile range (IQR)0.19994538

Descriptive statistics

Standard deviation0.1744879
Coefficient of variation (CV)0.23736909
Kurtosis0.33322734
Mean0.7350911
Median Absolute Deviation (MAD)0.090833333
Skewness-0.80749177
Sum879.90404
Variance0.030446028
MonotonicityNot monotonic
2024-03-06T15:34:45.533249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.800401961 24
 
2.0%
0.971866667 12
 
1.0%
0.850136766 12
 
1.0%
0.75065101 11
 
0.9%
0.850502311 11
 
0.9%
1.000230409 11
 
0.9%
0.800128721 8
 
0.7%
0.750395513 8
 
0.7%
0.858143939 7
 
0.6%
0.800117103 7
 
0.6%
Other values (869) 1086
90.7%
ValueCountFrequency (%)
0.233705476 1
0.1%
0.235795455 1
0.1%
0.238041667 1
0.1%
0.24625 1
0.1%
0.247316017 1
0.1%
0.249416667 1
0.1%
0.251399254 1
0.1%
0.2565 1
0.1%
0.258 1
0.1%
0.259375 1
0.1%
ValueCountFrequency (%)
1.1204375 1
0.1%
1.108125 1
0.1%
1.100483918 1
0.1%
1.096633333 1
0.1%
1.059621212 1
0.1%
1.057962963 1
0.1%
1.050666667 1
0.1%
1.05028058 1
0.1%
1.033570076 1
0.1%
1.033155556 1
0.1%

Interactions

2024-03-06T15:34:42.348512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:36.482825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:37.185846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:37.918403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.512597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.108329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.722436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.360645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.980484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.573757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:42.407268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:36.587955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:37.250349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:37.976824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.570521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.171490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.786563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.424225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.039805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.631677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:42.473550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:36.690822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:37.318361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.041084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.637053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.239793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.855326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.490138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.106466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.696089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:42.544110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:36.761403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:37.379162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.095999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.695085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.299445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.915734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.549460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.161598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.758063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:42.607289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:36.818764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:37.531201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.152572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.750099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.355611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.975368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.607474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.218284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.831616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:42.673076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:36.875530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:37.592474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.216150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.808076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.416524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.038793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.669003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.278024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.898229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:42.744367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:36.939732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:37.664665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.279425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.871246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.481819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.105050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.737840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.342246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.967039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:42.809915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:37image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:37.730056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.342087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.932021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.544020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.172085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.797210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.403837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:42.164336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:42.865942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:37.057966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:37.792028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.398927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.986541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.605008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.237139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.857039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.457745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:42.225443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:42.922100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:37.120824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:37.853577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:38.455527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.050414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:39.662015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.299375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:40.918801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:41.514470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-06T15:34:42.288218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-03-06T15:34:43.010862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-06T15:34:43.148792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datequarterdepartmentdayteamtargeted_productivitysmvwipover_timeincentiveidle_timeidle_menno_of_style_changeno_of_workersactual_productivity
01/1/2015Quarter1sweingThursday80.8026.161108.07080980.00059.00.940725
11/1/2015Quarter1finishingThursday10.753.94NaN96000.0008.00.886500
21/1/2015Quarter1sweingThursday110.8011.41968.03660500.00030.50.800570
31/1/2015Quarter1sweingThursday120.8011.41968.03660500.00030.50.800570
41/1/2015Quarter1sweingThursday60.8025.901170.01920500.00056.00.800382
51/1/2015Quarter1sweingThursday70.8025.90984.06720380.00056.00.800125
61/1/2015Quarter1finishingThursday20.753.94NaN96000.0008.00.755167
71/1/2015Quarter1sweingThursday30.7528.08795.06900450.00057.50.753683
81/1/2015Quarter1sweingThursday20.7519.87733.06000340.00055.00.753098
91/1/2015Quarter1sweingThursday10.7528.08681.06900450.00057.50.750428
datequarterdepartmentdayteamtargeted_productivitysmvwipover_timeincentiveidle_timeidle_menno_of_style_changeno_of_workersactual_productivity
11873/11/2015Quarter2sweingWednesday40.7526.821054.07080450.00059.00.750051
11883/11/2015Quarter2sweingWednesday50.7026.82992.06960300.00158.00.700557
11893/11/2015Quarter2sweingWednesday80.7030.48914.06840300.00157.00.700505
11903/11/2015Quarter2sweingWednesday60.7023.411128.04560400.00138.00.700246
11913/11/2015Quarter2sweingWednesday70.6530.48935.06840260.00157.00.650596
11923/11/2015Quarter2finishingWednesday100.752.90NaN96000.0008.00.628333
11933/11/2015Quarter2finishingWednesday80.703.90NaN96000.0008.00.625625
11943/11/2015Quarter2finishingWednesday70.653.90NaN96000.0008.00.625625
11953/11/2015Quarter2finishingWednesday90.752.90NaN180000.00015.00.505889
11963/11/2015Quarter2finishingWednesday60.702.90NaN72000.0006.00.394722